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Found 34 papers, 4 papers with code
Uncertainty-guided Contrastive Learning for Single Source Domain Generalisation
In the context of single domain generalisation, the objective is for models that have been exclusively trained on data from a single domain to demonstrate strong performance when confronted with various unfamiliar domains.
COVID-19 Computer-aided Diagnosis through AI-assisted CT Imaging Analysis: Deploying a Medical AI System
Computer-aided diagnosis (CAD) systems stand out as potent aids for physicians in identifying the novel Coronavirus Disease 2019 (COVID-19) through medical imaging modalities.
Domain adaptation, Explainability & Fairness in AI for Medical Image Analysis: Diagnosis of COVID-19 based on 3-D Chest CT-scans
The paper presents the DEF-AI-MIA COV19D Competition, which is organized in the framework of the 'Domain adaptation, Explainability, Fairness in AI for Medical Image Analysis (DEF-AI-MIA)' Workshop of the 2024 Computer Vision and Pattern Recognition (CVPR) Conference.
The 6th Affective Behavior Analysis in-the-wild (ABAW) Competition
This paper describes the 6th Affective Behavior Analysis in-the-wild (ABAW) Competition, which is part of the respective Workshop held in conjunction with IEEE CVPR 2024.
Distribution Matching for Multi-Task Learning of Classification Tasks: a Large-Scale Study on Faces & Beyond
Multi-Task Learning (MTL) is a framework, where multiple related tasks are learned jointly and benefit from a shared representation space, or parameter transfer.
Ranked #1 on
Facial Expression Recognition (FER)
on AffectNet
(Accuracy (7 emotion) metric, using extra
training data)
BTDNet: a Multi-Modal Approach for Brain Tumor Radiogenomic Classification
Brain tumors pose significant health challenges worldwide, with glioblastoma being one of the most aggressive forms.
ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Emotional Reaction Intensity Estimation Challenges
The fifth Affective Behavior Analysis in-the-wild (ABAW) Competition is part of the respective ABAW Workshop which will be held in conjunction with IEEE Computer Vision and Pattern Recognition Conference (CVPR), 2023.
FaceRNET: a Facial Expression Intensity Estimation Network
This paper presents our approach for Facial Expression Intensity Estimation from videos.
A Deep Neural Architecture for Harmonizing 3-D Input Data Analysis and Decision Making in Medical Imaging
Harmonizing the analysis of data, especially of 3-D image volumes, consisting of different number of slices and annotated per volume, is a significant problem in training and using deep neural networks in various applications, including medical imaging.
Multi-Label Compound Expression Recognition: C-EXPR Database & Network
In this paper we present an in-the-wild A/V database, C-EXPR-DB, consisting of 400 videos of 200K frames, annotated in terms of 13 compound expressions, valence-arousal emotion descriptors, action units, speech, facial landmarks and attributes.
Ranked #1 on
Facial Expression Recognition (FER)
on RAF-DB
(Avg. Accuracy metric)
ABAW: Learning from Synthetic Data & Multi-Task Learning Challenges
In more detail: i) s-Aff-Wild2 -- a static version of Aff-Wild2 database -- has been constructed and utilized for the purposes of the Multi-Task-Learning Challenge; and ii) some specific frames-images from the Aff-Wild2 database have been used in an expression manipulation manner for creating the synthetic dataset, which is the basis for the Learning from Synthetic Data Challenge.
AI-MIA: COVID-19 Detection & Severity Analysis through Medical Imaging
This paper presents the baseline approach for the organized 2nd Covid-19 Competition, occurring in the framework of the AIMIA Workshop in the European Conference on Computer Vision (ECCV 2022).
MixAugment & Mixup: Augmentation Methods for Facial Expression Recognition
We further investigate the combination of dropout with Mixup and MixAugment, as well as the combination of other data augmentation techniques with MixAugment.
Ranked #16 on
Facial Expression Recognition (FER)
on RAF-DB
ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Multi-Task Learning Challenges
This paper describes the third Affective Behavior Analysis in-the-wild (ABAW) Competition, held in conjunction with IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysis
In this paper we present the COV19-CT-DB database which is annotated for COVID-19, consisting of about 5, 000 3-D CT scans, We have split the database in training, validation and test datasets.
Analysing Affective Behavior in the second ABAW2 Competition
The Affective Behavior Analysis in-the-wild (ABAW2) 2021 Competition is the second -- following the first very successful ABAW Competition held in conjunction with IEEE FG 2020- Competition that aims at automatically analyzing affect.
Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Study
Based on this approach, we build FaceBehaviorNet, the first framework for large-scale face analysis, by jointly learning all facial behavior tasks.
Ranked #5 on
Facial Expression Recognition (FER)
on RAF-DB
(Avg. Accuracy metric, using extra
training data)
Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Framework
Affect analysis and recognition can be seen as a dual knowledge generation problem, involving: i) creation of new, large and rich in-the-wild databases and ii) design and training of novel deep neural architectures that are able to analyse affect over these databases and to successfully generalise their performance on other datasets.
Analysing Affective Behavior in the First ABAW 2020 Competition
For the Challenges, we provide a common benchmark database, Aff-Wild2, which is a large scale in-the-wild database and the first one annotated for all these three tasks.
Face Behavior a la carte: Expressions, Affect and Action Units in a Single Network
We present the first and the largest study of all facial behaviour tasks learned jointly in a single multi-task, multi-domain and multi-label network, which we call FaceBehaviorNet.
Interpretable Deep Neural Networks for Facial Expression and Dimensional Emotion Recognition in-the-wild
In this project, we created a database with two types of annotations used in the emotion recognition domain : Action Units and Valence Arousal to try to achieve better results than with only one model.
Interpretable Deep Neural Networks for Dimensional and Categorical Emotion Recognition in-the-wild
This project focuses on extending the emotion recognition database, and training the CNN + RNN emotion recognition neural networks with emotion category representation and valence \& arousal representation.
Image Generation and Recognition (Emotions)
Generative Adversarial Networks (GANs) were proposed in 2014 by Goodfellow et al., and have since been extended into multiple computer vision applications.
Emotion Generation and Recognition: A StarGAN Approach
The main idea of this ISO is to use StarGAN (A type of GAN model) to perform training and testing on an emotion dataset resulting in a emotion recognition which can be generated by the valence arousal score of the 7 basic expressions.
Aff-Wild Database and AffWildNet
VGGFace, ResNet, DenseNet with the corresponding pre-trained model for CNN block and LSTM, GRU, IndRNN, Attention mechanism for RNN block are experimented aiming to find the best combination.
AffWild Net and Aff-Wild Database
The purpose of this project is to study the previous work that was done for the "in the wild" emotions recognition concept, design a new dataset which has as a standard the "Aff-wild" database, implement new deep learning models and evaluate the results.
Exploiting multi-CNN features in CNN-RNN based Dimensional Emotion Recognition on the OMG in-the-wild Dataset
This paper presents a novel CNN-RNN based approach, which exploits multiple CNN features for dimensional emotion recognition in-the-wild, utilizing the One-Minute Gradual-Emotion (OMG-Emotion) dataset.
Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFace
The need to collect and annotate diverse in-the-wild datasets has become apparent with the rise of deep learning models, as the default approach to address any computer vision task.
Ranked #6 on
Facial Expression Recognition (FER)
on AffectNet
Deep Neural Network Augmentation: Generating Faces for Affect Analysis
This paper presents a novel approach for synthesizing facial affect; either in terms of the six basic expressions (i. e., anger, disgust, fear, joy, sadness and surprise), or in terms of valence (i. e., how positive or negative is an emotion) and arousal (i. e., power of the emotion activation).
Ranked #6 on
Facial Expression Recognition (FER)
on RAF-DB
(Avg. Accuracy metric, using extra
training data)
A Multi-Task Learning & Generation Framework: Valence-Arousal, Action Units & Primary Expressions
Various approaches have been proposed for: i) discrete emotion recognition in terms of the primary facial expressions; ii) emotion analysis in terms of facial Action Units (AUs), assuming a fixed expression intensity; iii) dimensional emotion analysis, in terms of valence and arousal (VA).
Aff-Wild2: Extending the Aff-Wild Database for Affect Recognition
The obtained results show premise for utilization of the extended Aff-Wild, as well as of the developed deep neural architectures for visual analysis of human behaviour in terms of continuous emotion dimensions.
Training Deep Neural Networks with Different Datasets In-the-wild: The Emotion Recognition Paradigm
A novel procedure is presented in this paper, for training a deep convolutional and recurrent neural network, taking into account both the available training data set and some information extracted from similar networks trained with other relevant data sets.
A Multi-component CNN-RNN Approach for Dimensional Emotion Recognition in-the-wild
This paper presents our approach to the One-Minute Gradual-Emotion Recognition (OMG-Emotion) Challenge, focusing on dimensional emotion recognition through visual analysis of the provided emotion videos.
Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond
Automatic understanding of human affect using visual signals is of great importance in everyday human-machine interactions.
